The future of gender research in small‐scale fisheries: Priorities and pathways for advancing gender equity
Bibliographic record
Abstract
Abstract This paper presents an agenda for the future of gender research in small‐scale fisheries (SSF). Building on expert insight from scholars who gathered during the 4th World Small‐Scale Fisheries Congress Africa (4WSFC) with a synthesis of existing literature, we identify six topics that warrant future investigation in SSF, along with methodological considerations for addressing them. Research priorities include identifying pathways towards (1) equitable participation in governance and decision‐making, (2) valuing all actors' contributions to aquatic food systems, (3) increasing access to financial services, (4) inclusive infrastructural development, (5) livelihood diversification and (6) reducing occupational health hazards. Several important methodological considerations include (i) using multiple methodologies, (ii) applying participatory methods, (iii) collecting gender‐disaggregated data, (iv) integrating gender into a food systems approach in fisheries, (v) engaging an intersectional approach and (vi) operationalising equity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".